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  <div class="headertitle"><div class="title">shark::RFTrainer&lt; LabelType &gt; Class Template Reference<div class="ingroups"><a class="el" href="group__supervised__trainer.html">Supervised Trainers</a></div></div></div>
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<p>Random Forest.  
 <a href="classshark_1_1_r_f_trainer.html#details">More...</a></p>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><div class="compoundTemplParams">template&lt;class LabelType&gt;<br />
class shark::RFTrainer&lt; LabelType &gt;</div><p>Random Forest. </p>
<p>Random Forest is an ensemble learner, that builds multiple binary decision trees. The trees are built using a variant of the CART methodology</p>
<p>Typically 100+ trees are built, and classification/regression is done by combining the results generated by each tree. Typically the a majority vote is used in the classification case, and the mean is used in the regression case</p>
<p>Each tree is built based on a random subset of the total dataset. Furthermore at each split, only a random subset of the attributes are investigated for the best split</p>
<p>The node impurity is measured by the Gini criteria in the classification case, and the total sum of squared errors in the regression case</p>
<p>After growing a maximum sized tree, the tree is added to the ensemble without pruning.</p>
<p>For detailed information about Random Forest, see Random Forest by L. Breiman et al. 2001. </p>

<p class="definition">Definition at line <a class="el" href="_r_f_trainer_8h_source.html#l00073">73</a> of file <a class="el" href="_r_f_trainer_8h_source.html">RFTrainer.h</a>.</p>
</div><hr/>The documentation for this class was generated from the following file:<ul>
<li>include/shark/Algorithms/Trainers/<a class="el" href="_r_f_trainer_8h_source.html">RFTrainer.h</a></li>
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